Aircraft runway occupancy time prediction based on ensemble learning
Arrival runway occupancy time prediction model based on ensemble learning is proposed to accurately predict the time of an aircraft occupying the runway during landing.Firstly,the influencing factors are obtained by processing the onboard QAR(Quick Access Recorder)data,and their correlation is ranked using the Pearson product-moment correlation coefficient.Secondly,a prediction model based on stacking ensemble learning strategy is constructed to predict the arrival runway occupancy time.Fi-nally,the prediction accuracy of different models is compared by calculating model evaluation metrics.Experimental results demon-strate that the proposed ensemble learning prediction model achieves higher accuracy and provides a theoretical basis for improving the operational efficiency of airports.
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